Boris Johnson is often portrayed as a flawed optimist, promoting liberty for too long during the pandemic until events back him into a corner and he is forced to U-turn.
This characterisation of the UK prime minister has its roots in the economic concept of constrained optimisation. To manage Covid-19, the theory dictates that policymakers should strive for the best health and economic outcomes, while taking account of constraints such as hospital capacity, available treatments, the prevalence of the virus and imperfect data on the course of the pandemic.
Using this frame of thinking, he is guilty of reacting too slowly as constraints have changed and therefore failing to implement the optimal policy either for health or economics — as illustrated in this month’s abrupt reimposition of national lockdown after weeks of attempting to control rising infection rates with tiered regional restrictions.
But constrained optimisation is far from the only way of setting policy in the face of severe risks. Engineering and financial regulation lean towards concepts of robustness and resilience instead. These ditch the ambition of fine tuning policy to create the best results each time the constraints change and replace it with darker thoughts of what would be safest if things go wrong. Policies set with robustness in mind would never get the best outcomes, but would avoid the worst.
If we look back over the past year, there are many instances where a robustness framework would have been far superior to constrained optimisation.
Early in the pandemic, the NHS continued to release the most vulnerable patients from hospitals to care homes in a bid to increase the number of available beds for Covid-19 patients. This was a classic optimisation decision with disastrous consequences as the virus spread uncontrolled through the sector, contributing to 26,000 excess deaths in care homes in England and Wales from March to November, killing about 10 per cent of residents.
Large financial incentives over the summer to encourage people to eat in pubs and restaurants helped the hospitality sector’s financial troubles at the same time as increasing the virus’s spread. How much it contributed to the autumn second wave is hard to say, but there is no doubt the policy was set without the mindset of what could go wrong.
The autumn’s coronavirus control system had optimality written all over it. Seeking to fine tune different regions into multiple tiers sufficiently precisely to keep the reproduction number of the virus around one, enable hospitals to cope with a steady stream of new patients and keep the economy firing as fast as possible, assumed much more control than was possible.
There were many potential flaws, including the uncertain spread of the virus from schools to the wider community and individual behaviour around social distancing. No one should have been surprised that a new, more transmissible variant of Covid-19 could wreck the policy. But it was a direct consequence of the choice of an optimal rather than robust policy.
In short, the outcome of ministers using a constrained optimisation framework has been more illness, more death and worse economic performance.
Of course, choices are easier with the benefit of hindsight. But the government is sticking with constrained optimisation even after these failures. It is still unwisely wedded to a return to fine tuning local restrictions.
But let no one say these are simple decisions. On vaccines, for example, the government has again plumped for optimisation. Its decision to delay the second doses of both the jabs approved for use in the UK will enable the health service to inoculate more people more quickly with a single dose over the next three months.
What could go wrong? With no evidence of persistent efficacy from a single dose, immunity might lapse quickly leading to precious little benefit from vaccination in the worst-case scenario and no herd immunity. Epidemiologists are divided on whether this is a sensible strategy.
Ministers might well be right this time in their latest optimising gamble with Covid-19 and the UK population. But repeatedly playing double or quits and losing should surely prompt a change of strategy. When dealing with severe risks, there should be fewer decisions based on optimisation and more account taken of robustness.
Get alerts on Coronavirus pandemic when a new story is published